- Title
- Educational big data : predictions, applications and challenges
- Creator
- Bai, Xiaomei; Zhang, Fuli; Li, Jinzhou; Guo, Teng; Xia, Feng
- Date
- 2021
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180009
- Identifier
- vital:15679
- Identifier
-
https://doi.org/10.1016/j.bdr.2021.100270
- Identifier
- ISBN:2214-5796 (ISSN)
- Abstract
- Educational big data is becoming a strategic educational asset, exceptionally significant in advancing educational reform. The term educational big data stems from the rapidly growing educational data development, including students' inherent attributes, learning behavior, and psychological state. Educational big data has many applications that can be used for educational administration, teaching innovation, and research management. The representative examples of such applications are student academic performance prediction, employment recommendation, and financial support for low-income students. Different empirical studies have shown that it is possible to predict student performance in the courses during the next term. Predictive research for the higher education stage has become an attractive area of study since it allowed us to predict student behavior. In this survey, we will review predictive research, its applications, and its challenges. We first introduce the significance and background of educational big data. Second, we review the students' academic performance prediction research, such as factors influencing students' academic performance, predicting models, evaluating indices. Third, we introduce the applications of educational big data such as prediction, recommendation, and evaluation. Finally, we investigate challenging research issues in this area. This discussion aims to provide a comprehensive overview of educational big data. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record**
- Publisher
- Elsevier Inc.
- Relation
- Big Data Research Vol. 26, no. (2021), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 Elsevier Inc.
- Rights
- Open Access
- Subject
- 0104 Statistics; 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; Educational big data; Educational data mining; Performance prediction; Predictive models
- Full Text
- Reviewed
- Funder
- This work was partially supported by Department of Science and Technology of Liaoning Province (Liaoning Provincial Key R&D Guidance Project 2018104021 ).
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